Enabling Scalable Kinesthetic Teaching via Observer-based Hand-guiding with Active Support

2026-08-11

AI Summary

This work addresses the limitations of conventional passive robotic hand guiding, which often leads to operator fatigue, degraded demonstration quality, and poor scalability, while existing active approaches typically rely on costly sensors or task-specific priors. The paper reframes hand guiding as an active human–robot collaborative interaction and, for the first time, achieves intent-aware assistance without additional hardware by integrating observer-based external torque estimation, joint torque sensing, redundant kinematics, gravity compensation, and noise suppression. A user study with 16 participants on the KUKA LWR iiwa platform demonstrates that the proposed method significantly reduces physical workload, enhances performance in both fine and agile manipulation tasks, and elicits strong user preference, thereby overcoming the fundamental constraints of traditional passive control paradigms.

Abstract

Kinesthetic teaching through robot hand-guiding provides a natural interface for collecting demonstrations in imitation learning and programming-by-demonstration. However, extended sessions cause operator fatigue, reducing demonstration quality and limiting scalability. Current industrial hand-guiding approaches typically provide no active assistance, and alternatives require costly wrist-mounted force-torque sensors or rely on learned motion priors unavailable for new tasks. We propose RHOAS, a hand-guiding scheme that actively supports operator-intended motions using model-based force estimation without additional hardware. Our approach considers robot hand-guiding as an actively controlled interaction by the human operator, rather than an interaction with a passive environment. Standard methods used for hand-guiding typically rely on general passivity-based compliant control architectures that unnecessarily increase operator effort and limit the range of demonstrable motions without providing the intended stability guarantees in active interaction. Instead, our design utilizes model-based external torque estimation, internal joint torque sensing, and redundant robot kinematics to actively support human physical input within the human interaction frequency bandwidth. We address practical challenges of relying on observer-based force estimation, including suppression of unmodeled joint elastic dynamic effects and measurement noise in the feedback path, reduced estimate accuracy close to kinematic singularities, and static gravity compensation errors. In a user study with 16 participants on a KUKA LWR iiwa we demonstrate statistically significant reductions in physical effort, improved maneuverability for both precise and agile tasks, and clear user preference.

Problem

• kinesthetic teaching
• hand-guiding
• operator fatigue
• imitation learning
• physical human-robot interaction

Innovation

• kinesthetic teaching
• hand-guiding
• model-based force estimation
• active compliance
• observer-based control